AZtrauma

International Multicenter Validation of an Expanded AI Diagnostic System for 18 Pathologies in Thoracic and Musculoskeletal Radiography

Performance of a complete AI radiographic suite across 258,373 X-rays from 26 countries: A worldwide evaluation

Novel Artificial Intelligence Chest X-ray Diagnostics: A Quality Assessment of Their Agreement with Human Doctors in Clinical Routine

Enhancing Pediatric Fracture Detection: Multicenter Evaluation of a Deep Learning AI Model and Its Impact on Radiologist Performance

Clinical evaluation of two artificial intelligence algorithms in standard radiography for post-traumatic exploration of peripheral limbs in children

Evaluation of commercial AI algorithms for the detection of fractures, effusions, and dislocations on real-world clinical data: A prospective registry study

Real-world clinical impact of three commercial AI algorithms on musculoskeletal radiography interpretation: A prospective crossover reader study

Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs

Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms

Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children

External validation of a commercially available deep learning algorithm for fracture detection in children

How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?